I am wanting to interpret the results I obtained by applying ANOVA, but I get a complex and very small value (Pr(>F) , I am seeing if I can conclude something or it is not significant at all.
Go the results I got:
Df Sum Sq Mean Sq F value Pr(>F)
sectorlaboral 1 786 786.5 70.64 2.52e-16 ***
Residuals 677 7537 11.1
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Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
I answer in regards to R, which is the theme of this space. Unfortunately there is no stat.stackexchange.com, which would be the appropriate place for questions about interpretation of models.
What you call "complex value", if I understand you well, is a number that R reports in scientific notation and that more conventionally we would express as 0.000000000000000252, 16 zeros before your first value. So that R does not express that type of number with scientific notation you can adjust the limit from which he uses it with the following option
options(scipen=999)
This way you do not use scientific notation until you have a number with 999 zeros before the first number greater than zero. I mean, practically never.
About the interpretation of the p-value: it depends on what you are asking the model and how you specified it.
Assuming that you are doing a one-way ANOVA, it would be interpreted that the probability that a random distribution with the same true mean as the reference level contains the value that your model estimates for labor is very low , and therefore you can reject the nullity hypothesis that the means are equal.
There is a very interesting debate today about the use of p-value for hypothesis testing, but it seems to me that this is not the place for that debate. I hope they open es.stats.stackexchange.com.